The Connected Effect

This is the fifth and final post in a multi-part series, which specifically explores the challenges of dealing with wireless technology as part of an M2M (Machine-To-Machine) initiative. Today’s post will focus on data storage and application development.

In our first four posts, we’ve covered the key steps for establishing, managing, maintaining, and securing wireless M2M connectivity. But all of this leads up to the one essential question:

How will you use all that data?

And for a dose of truth: lots of data is pretty meaningless if you don't have a plan for it.

The ability to turn wireless machine data into consumable and useful information is critical to making an M2M initiative successful and impacting your organization's bottom line. But there isn't always a clear path, and it can be awfully challenging to see the promise land when you're buried in facts and figures.

In its raw form, machine data is arcane, proprietary, and not very usable for most organizations. Businesses need tools and strategies to make raw data easy to consume, and need to come up with a data model and programmatic interfaces that make it easy for programmers to develop applications and integrate machine data into other systems.

Here are four key steps that businesses should take to make machine data consumption and integration easier:

Understand the originating data formats. With no real standard for M2M communications, M2M data is highly fragmented and often varies from device to device. There’s a difficult learning curve involved, but understanding the data formats you’ll be using with different devices will help you prepare to translate it into formats you can more easily deal with.

Normalize the data. Store machine data in a normalized format regardless of the device sending the data. For example, trip records from vehicle devices are very different depending on the device supplier, but for most of them you can extract common information: the start time, end time, and points hit along the way. Regardless of the device used, store the information the same way. Consider using a relational database or data repository that you are familiar with. This will enable you to manage the historical data more effectively and efficiently.

Expose the data using modern APIs (like REST or SOAP) to turn raw data access into familiar API access. This will improve developer productivity.

Make it scalable. Rest assured – your M2M initiative will grow, whether by bringing new machines onto the network, or retrofitting older ones for connectivity. Ensuring that your data storage and access architecture is built to handle the influx of data is key.

Perhaps the biggest challenge of making M2M data usable is that it involves a lot of low-level designing and application logic which can be time-consuming and tedious. Leveraging M2M/IoT platforms that are device-agnostic, can handle massive amounts of data, and include elegant APIs out of the box will dramatically reduce the time needed to translate and manage machine data, and accelerate your time to market for new applications and integrations.